neural networks | Investment: How neural networks are used in the investment world

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Artificial intelligence (AI) is used to develop complex algorithms for a range of use cases – it has found applications across all sectors, including trade and investment.

You can now use neural networks to make predictions, using the best data available to make trading and investing decisions based on AI predictions.

The data used in a neural network must be coupled with human decision making. Neural networks can also provide a comprehensive investment strategy for your short-term and long-term investment goals.

Let’s begin our discussion of how neural networks are used in the investment world.

What are neural networks?

Before understanding the use of the neural network in investing, you need to understand the term better.

Neural networks, also known as artificial neural networks (ANNs), use a variety of data pointers to categorize information and make predictions.

With neural networks, you can feed historical data to any algorithm to inform it. It might sound like traditional modeling to those familiar with it.

However, neural networks are more advanced – they include a self-learning component. Your algorithm learns and optimizes its results based on new data 24/7.

So why is it called the neural network? It takes its name from the neural networks in the human brain – it serves as a model for this form of AI.

Each artificial neuron has a specific threshold and weight. If your input data is above the threshold, the neuron passes the data to the next layer.

As you increase the amount of input (training) data, the accuracy of the neural network increases with each calculation.

As you continue to use neural networks for active trading and investing, the AI ​​will continue to optimize its performance and adjust its weighting.

Using Neural Networks in Investing

Neural networks help you develop strategies based on your overall investment strategy: high-risk but growth-oriented (short-term trades) or a conservative approach to long-term investing.

Let’s get one thing straight: neural networks don’t create stock forecasts – they help investors evaluate new opportunities. Let’s understand this with an example:

Let’s say you’re looking for public companies that match the high-growth performance of one of the existing companies in your portfolio.

In such a situation, the neural network can highlight a handful of other companies with similar fundamentals that will make your job easier.

The example above has a single data pointer as input. You can have N number of data pointers to find new investment opportunities.

How do companies use neural networks?

Companies use a neural network to input their proven investment and trading ideas. Once implemented, you get data to help you figure out how effective your idea is likely to be – something traditional AI models can’t do.

A neural network allows you to modify your idea by changing the parameters for the data inputs you want to consider. We have used this function to modify JARVIS over and over again. It helps us to improve our platform.

Let’s discuss using neural networks in a real investment problem: Suppose we have a diversified portfolio and we want to manage the portfolio according to a given set of rules, for example based on the risk profile.

We can build a neural network that acts on investors’ portfolios by training it on specific instructions. How can it be implemented? Assume a diversified portfolio where the requirement is to mitigate systematic risk.

To achieve this, we can train a neural network to liquidate a portfolio (or any stock) if it falls X% on any given day – it acts like a personal circuit breaker.

The best way to implement this strategy is not to follow a discrete set of rules, but rather a continuous set of rules that are regularly updated through a combination of reinforcement, unsupervised, and online supervised learning algorithms.

Conclude

Every investor needs to know about market trends. However, due to the dynamics of the market, it is a big challenge to predict the stock price.

Stock markets are mostly non-parametric, noisy, non-linear and deterministic chaotic systems.

However, time has changed – due to the availability of a remarkable amount of data and technological advances, we can now formulate a suitable algorithm for prediction, the results of which can increase profits for investors.

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